Platform/Learning loop
A correction made once should never have to be made again.
In most deployments the person who spots the mistake fixes that one answer, and the same mistake arrives again on Thursday. This is the part that stops that.
What one correction becomes
Someone fixes an answer on a Tuesday. Four things happen to it.
A correction arrives as an ordinary act of work. Reviewed once, it fans out into every place it needs to reach.
The loop, in three turns
Humans correct. Agents act. Your systems return what actually happened.
Each turn produces a record no public model has seen, because it only exists inside your work.
TURN 01
A person corrects
In chat, in Slack, in the tool they already work in. No form, no ticket, no separate labelling exercise bolted on to their week.
“T3 billing escalations never go by SMS. Email finance and copy the account owner.”
TURN 02
Agents act on it
Once reviewed and promoted, the rule reaches every agent through the brain, whichever harness or model they happen to be running on.
The next run routes to email without being told again.
TURN 03
Systems return the result
The decision trace holds the input state, the action and what came back, so the value of a change is measured rather than assumed.
Did the escalation actually resolve faster.
Review sits between the first turn and the second on purpose.
What decides a promotion
An agent moves up when it can show it got better.
Public benchmarks cannot help. The exceptions that make your business yours are exactly what a general eval has never heard of.
01
Your team writes the cases
Ten or twenty real ones, each with the answer you would have given.
02
Every change gets replayed
A new skill, an edited rule, a swapped model. All of it runs against the whole set before it reaches production, so you see the effect of a change in advance.
03
The score gates the rung
Assisted, reviewed, supervised, autonomous. Each rung sits behind a threshold your team sets, and a drop blocks the promotion and names which cases moved.
04
Corrections become new cases
When someone fixes an answer in the flow of work, that becomes a case the agent is measured on from then on.
Where this actually is today
Tracing and correction capture run in production now. Every run and every fix is already recorded against the context pack that produced it, which is the part you cannot go back and recreate later.
The replay harness and threshold gating are in build. Scoring against measured business outcomes rather than against a written answer is the piece after that.
Earning autonomy
Autonomous never means unsupervised, and it is granted per function.
One function can run on its own while the one next to it is still drafting. What moves a function up is evidence.
Better context and stronger evals are what let more functions climb.
Where this is going
The same record is what a model of your own would be trained on.
A decision trace holds the context, the action, the correction and the outcome. That is the shape of a training example, and it accrues on your infrastructure.
What the training environment looks like →Bring ten cases you already argue about.
The clearest way to see whether this holds is to hand it the exceptions your team knows a general model gets wrong, and watch what happens the second time.